# Plotting for Parity Advanced
#plotga1 <- function(exppar){
    (palette(gray(seq(0,0.8,len=6))))
    # Indexing
    idx2 <- seq(4,100,by=4)

    # Set graph parameters
    #par(mfrow=c(2,1))

    
    # Plot ObjF
    # Set data object
    obj <- pn06a.objf
    postscript("par06adv.objf.ps", paper="special", width=7, height=5,horizontal=F)
    par(pin=c(5,3))
    # Average
    a0301 <- apply(pn06.objf$at.as,1,mean)
    a0406 <- apply(pn06.objf$co.wi,1,mean)
    a0515 <- apply(obj$as.ai,1,mean)
    a0516 <- apply(obj$as.wi,1,mean)
    a0525 <- apply(obj$ws.ai,1,mean)
    a0526 <- apply(obj$ws.wi,1,mean)
    # Main trend
    plot(a0301[1:25],type="l",lty=6,col=6,lwd=2,ylim=c(0.4,0.7),xlim=c(0,25),
         xlab="Number of Interaction (*64)",ylab="Best Fitness")
    lines(a0406[idx2],type="l",lty=5,col=5,lwd=2)
    lines(a0515[idx2],type="l",lty=4,col=4,lwd=1)
    lines(a0516[idx2],type="l",lty=3,col=3,lwd=1)
    lines(a0525[idx2],type="l",lty=2,col=2,lwd=1)
    lines(a0526[idx2],type="l",lty=1,col=1,lwd=1)
    # Legend
    legend(1,0.47,ncol=2,
           c("GAAS*","COWI*","ASAI","ASWI","WSAI","WSWI"),
           lwd=c(2,2,1,1,1,1),
           lty=c(6,5,4,3,2,1),
           col=c(6,5,4,3,2,1),
           cex=0.8)
    title("Averaged Best Objective Fitness")
    dev.off()
    
    # Closing device and clean up
    #dev.off()
    rm(idx2, obj,
       a0301,a0406,a0515,a0516,a0525,a0526
    )
#}
